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How this skill is triggered — by the user, by Claude, or both
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/webflow-content-creator:webflow-content-creatorThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
---
Create compelling, SEO-optimised content for the SAS-AM website and publish directly to Webflow CMS. Write like a knowledgeable peer who's spent time on the tools, understands asset management realities, and can translate complex concepts into actionable insights.
This skill helps you:
This skill accepts a topic, brief, or content type as its primary input. It can also work from research dossiers or existing material.
/webflow-content-creator Write an article about AI readiness for asset managers
/webflow-content-creator Case study: Mining company predictive maintenance success
/webflow-content-creator Article based on b2b-research-agent research for [company]
/webflow-content-creator Promote our new risk assessment service
/webflow-content-creator Technical article on Power Law Process for reliability
The skill MUST interview the user before drafting any content. This is non-negotiable.
The skill never invents stories, quotes, statistics, outcomes, or client experiences. Every claim, number, and narrative element must come directly from the user's interview answers or verified research. If the user hasn't provided evidence, the skill does not fabricate it.
Ask first:
"What type of content are you creating?
- Article — thought leadership, technical insight, industry analysis
- Case Study — client success story with measurable outcomes"
| Question | Purpose |
|---|---|
| Core topic | "What is the main topic or insight for this content?" |
| Primary sector | "Which sector is most relevant? (Local Government, Water, Resources & Minerals, Transport, Health, Defence)" |
| Topic tags | "Which tags apply? (AI, Asset Management System, Technical, Insight, Asset Condition, Machine Learning, Advisory)" |
| Target audience | "Who is the primary reader? (executives, technical practitioners, asset managers, decision-makers)" |
| Key takeaway | "What single insight should readers remember after reading?" |
| CTA goal | "What action should readers take? (contact us, download resource, explore related content)" |
| Research available | "Do you have existing research from b2b-research-agent to incorporate?" |
| Hero image | "Do you have an image, or should I generate one using nano-banana-2?" |
| Question | Purpose |
|---|---|
| Content angle | "Is this educational (how-to), opinion (thought leadership), analytical (data-driven), or news (industry update)?" |
| Supporting evidence | "What data, studies, or sources support this? The skill will not fabricate statistics." |
| Related content | "Are there existing SAS-AM articles this should link to?" |
| Technical depth | "Should this be accessible to all readers or go deep technically?" |
| Question | Purpose |
|---|---|
| Client identification | "Can you name the client, or should this be anonymised (e.g., 'a major water utility')?" |
| The challenge | "What specific problem were they facing before engagement?" |
| The solution | "What did SAS-AM do? Which services/approaches were applied?" |
| The outcomes | "What measurable results were achieved? (Must be specific: %, $, timeframes)" |
| Client testimonial | "Do you have a quote from the client? (If not, content will avoid fabricated quotes)" |
| Publication permission | "Has the client approved publication? (Required for named case studies)" |
| Timeline | "What was the project timeline?" |
If the user says "just draft something" or tries to skip the interview:
"I need real material to work with — the best content comes from real experiences, not invented ones. Let me ask you a few quick questions to surface the good stuff. This takes 2 minutes and makes the difference between generic content and something that genuinely resonates with your audience."
Catalogue what real material was gathered:
=== Material Inventory ===
GATHERED:
- Core topic: [identified]
- Sector: [selected]
- Tags: [selected]
- Story/anecdote: yes/no
- Measurable outcomes: yes/no
- Client quote: yes/no
- Supporting research: yes/no
- Image: provided/generate/placeholder
CONTENT TYPE: Article / Case Study
READY TO DRAFT: Yes / No (list missing essentials)
| Trait | What it means | What it doesn't mean |
|---|---|---|
| Upbeat | Optimistic about technology's potential, energised by solving problems | Sycophantic, fake positivity, ignoring real challenges |
| Clear & Concise | Get to the point, respect the reader's time, no waffle | Dumbed down, oversimplified, missing nuance |
| Tech Forward | Embrace AI/ML, analytics — grounded in practical application | Buzzword-heavy, hype-driven, technology for its own sake |
| Insightful | Offer genuine value, perspectives others haven't considered | Stating the obvious, regurgitating common knowledge |
| Playful | Occasional wit, relatable analogies, don't take ourselves too seriously | Unprofessional, silly, undermining credibility |
| Conversational | Write like explaining to a smart colleague over coffee | Overly formal, academic, stiff corporate-speak |
SAS-AM is an Australian asset management consulting firm specialising in:
| Sector | Key Themes |
|---|---|
| Local Government | Community assets, limited budgets, regulatory compliance, long-term planning |
| Water | Critical infrastructure, pump stations, treatment plants, climate resilience |
| Resources & Minerals | Heavy equipment, production optimisation, safety-critical systems |
| Transport | Fleet management, infrastructure networks, service reliability |
| Health | Medical equipment, facility management, compliance, patient safety |
| Defence | Security requirements, sovereign capability, mission-critical systems |
# [HEADLINE]
**Sector**: [PRIMARY_SECTOR] | **Topics**: [TOPIC_TAGS]
---
## The Hook (50-100 words)
[Opening paragraph that captures attention and establishes relevance.
Ask a provocative question, state a surprising fact, or name a common frustration.]
## The Context (100-200 words)
[Setting the scene — why this matters now, what's changing in the industry.
Connect to reader's current challenges.]
## The Core Content (400-800 words)
### [Key Point 1]
[Detailed explanation with evidence]
### [Key Point 2]
[Detailed explanation with evidence]
### [Key Point 3] (if applicable)
[Detailed explanation with evidence]
## The Practical Application (150-250 words)
[How readers can apply this information — actionable takeaways.
Be specific: "Do this tomorrow" not "consider doing this eventually."]
## The Connection (50-100 words)
[Call to action — what to do next, related resources, contact.
Make the next step clear and easy.]
---
**About SAS-AM**: SAS Asset Management is an Australian consulting firm...
**Related Resources**: [Links to related content]
| Type | Word Count | When to Use |
|---|---|---|
| Short insight | 500-800 words | Single focused idea, quick read |
| Standard article | 800-1200 words | Full exploration of topic |
| Deep dive | 1200-2000 words | Technical content, comprehensive guides |
# [HEADLINE — Outcome-Focused]
**Sector**: [PRIMARY_SECTOR] | **Type**: Case Study | **Topics**: [TOPIC_TAGS]
---
## Executive Summary (50-100 words)
[One-paragraph summary: who, what challenge, what outcome.
Lead with the result to hook the reader.]
## The Challenge (150-250 words)
[What problem the client faced — specific, evidenced]
**Pain Points:**
- [Pain point 1]
- [Pain point 2]
- [Business impact]
## The Approach (200-300 words)
[What SAS-AM did — methodology, services applied]
**Key Phases:**
1. [Phase 1: Description]
2. [Phase 2: Description]
3. [Phase 3: Description]
**Key Differentiators:**
- [What made SAS-AM's approach different]
## The Outcomes (150-250 words)
[Measurable results achieved — be specific]
| Metric | Before | After | Improvement |
|--------|--------|-------|-------------|
| [Metric 1] | [Before] | [After] | [% or $ improvement] |
| [Metric 2] | [Before] | [After] | [% or $ improvement] |
> "[Client quote if available]" — [Name], [Title]
## Key Learnings (100-150 words)
[What others can learn from this project — transferable insights]
## Ready to Achieve Similar Results? (50-75 words)
[CTA — contact, consultation, related services]
---
**Project Details**: [Sector], [Timeline], [Services Applied]
H1: One per page — the main title
H2: Major sections (3-5 per article)
H3: Subsections within H2s
Before publishing, verify:
| Tool | Purpose |
|---|---|
mcp__webflow__sites_list | List available Webflow sites |
mcp__webflow__collections_list | List CMS collections for a site |
mcp__webflow__collections_items_list_items | List existing items in a collection |
mcp__webflow__collections_items_create_item | Create new CMS item (draft) |
mcp__webflow__collections_items_create_item_live | Create and publish immediately |
mcp__webflow__assets | Upload images/media |
Phase 1: Site Discovery
1. Call sites_list to find sas-am.com site
2. Extract site_id
Phase 2: Collection Discovery
1. Call collections_list with site_id
2. Identify the Resources collection (for articles/case studies)
3. Note field schema for validation
Phase 3: Asset Upload (if hero image generated)
1. Upload hero image via assets endpoint
2. Retrieve asset URL for CMS item
Phase 4: Create Content Item
1. Prepare CMS payload with all required fields
2. Call collections_items_create_item with isDraft: true
3. Return item_id and preview URL for review
Phase 5: Publish (after user review)
1. User confirms content is ready
2. Call collections_items_publish_items
3. Confirm live URL
Reference the schema in references/cms-schema.json for exact field names and types.
Required fields:
name — Article/case study titleslug — URL-friendly version of titlesector — Primary sector (single select)content-type — "Article" or "Case Study"topic-tags — Array of tagsfeatured-image — Hero image URLdescription — Meta description (max 160 chars)body-content — Full content (Rich Text)seo-title — Title tag (max 60 chars)seo-description — Meta description| Error | Action |
|---|---|
| Site not found | List available sites, ask user to confirm |
| Collection not found | List collections, ask user to select |
| Field validation error | Report specific field, show expected format |
| Asset upload failure | Retry or use placeholder |
| Publishing failure | Save content locally, provide recovery path |
Offer hero image generation when:
Analyse the content to derive an image prompt:
[SCENE derived from content topic]
[SETTING — industrial, office, outdoor, etc.]
[EMOTIONAL TONE — professional, dramatic, optimistic]
| Content Topic | Image Prompt |
|---|---|
| Water utility AI implementation | "Water treatment facility control room with digital monitoring displays, professional engineer analysing data, dramatic industrial lighting" |
| Mining maintenance success | "Heavy mining equipment with modern sensors and data cables, Australian outback setting, golden hour lighting" |
| Asset management maturity | "Professional team reviewing analytics dashboard in modern boardroom, collaborative atmosphere, natural lighting" |
/nano-banana-2 "[generated prompt]" --aspect 16:9
The skill will:
Invoke b2b-research-agent when:
/b2b-research-agent [topic or company]Research-sourced content must include:
=== Research Integration ===
Source: b2b-research-agent dossier for [Company]
Date: [Date]
Key findings incorporated:
- [Finding 1] — cited in paragraph 3
- [Finding 2] — cited in outcomes section
- [Statistic] — attributed to [Source]
Run the content type selection, then the appropriate interview questions. Do not proceed until material is gathered.
If user requested research integration:
/b2b-research-agent [topic or company]Based on content type:
Apply all voice, tone, and SEO guidelines.
If user requested image generation:
/nano-banana-2 with 16:9 aspect ratioPresent the complete draft:
=== DRAFT PREVIEW ===
[Content preview with all sections]
=== END PREVIEW ===
Questions:
- Does this capture your intent?
- Any facts or figures to adjust?
- Ready to proceed to Webflow, or need revisions?
If user approves:
=== PUBLISHED ===
Status: Live
URL: [live URL]
Preview took: [X] seconds
Content is now visible on www.sas-am.com/resources
Before presenting final content, verify:
The skill responds to these in-session commands:
| Command | Action |
|---|---|
interview | Run the discovery interview |
article | Create a new article |
case-study | Create a new case study |
research [topic] | Invoke b2b-research-agent |
draft | Generate content without publishing |
image | Generate hero image via nano-banana-2 |
seo | Generate/regenerate SEO metadata |
preview | Show formatted content preview |
checklist | Run quality checklist |
publish-draft | Create draft in Webflow CMS |
publish-live | Publish directly to live site |
status | Check Webflow connection status |
sectors | List available sectors |
tags | List available topic tags |
# Why Your AI Pilot Failed: Data Quality is the Hidden Killer
**Sector**: Resources & Minerals | **Topics**: AI, Machine Learning, Asset Management System
---
Most AI projects in asset management don't fail because of bad algorithms.
They fail because of data.
Specifically, they fail because organisations underestimate what "AI-ready data" actually means — and overestimate how close they are to having it.
After working on dozens of implementations across mining, water, and transport, we've seen the same pattern emerge:
## The Four Data Quality Killers
### 1. Inconsistent CMMS Records
Your maintenance management system is only as good as the data going into it. When work orders use free text instead of structured fields, when failure codes are applied inconsistently, when one site does things differently from another — the AI has nothing reliable to learn from.
### 2. Disconnected Sensor Data
Many organisations have invested in condition monitoring systems. Vibration sensors, temperature probes, flow meters. But the data sits in its own silo, disconnected from asset hierarchies and maintenance history. The AI can't connect cause and effect.
### 3. Missing Failure History
AI learns from patterns. If you don't have reliable records of what failed, when it failed, and why — there's no pattern to learn. "Equipment failed" isn't useful. "Bearing failure due to inadequate lubrication after 18 months of operation" is.
### 4. No Data Ownership
Here's the one that kills most projects: nobody owns data quality as an ongoing discipline. It's everyone's job, which means it's nobody's job. Cleanup projects come and go, but the underlying behaviours don't change.
## What Actually Works
The organisations successfully using AI started with what they had and improved the data as part of the AI project, not before it.
Focus on:
- **Critical assets only** — don't try to clean everything
- **Target failure modes** — pick the failures that cost you the most
- **2-3 years of history** — not decades
- **One data quality owner** — not a committee
## The Honest Answer
AI readiness is 80% data work and 20% model work. The "we're not ready" story feels safe. But it's often a way to avoid the harder conversation: "we don't know where to start."
Start small. Pick one critical asset. Clean that data. Build one model. Learn.
---
**Ready to assess your AI readiness?** Contact SAS-AM for a practical assessment that identifies your quickest wins.
**Related**: [5 Signs Your Data Isn't AI-Ready], [The ISO 55001 Data Quality Connection]
# Gold Mining Maintenance: 23% Reduction in Unplanned Downtime
**Sector**: Resources & Minerals | **Type**: Case Study | **Topics**: Asset Condition, Machine Learning, Advisory
---
## Executive Summary
A major Australian gold producer reduced unplanned equipment downtime by 23% across their processing facility by implementing condition-based maintenance with predictive analytics. The project delivered $1.4M in annual savings within 12 months.
## The Challenge
The client operated a gold processing facility with over 200 rotating assets including crushers, mills, and conveyors. Their maintenance approach was predominantly calendar-based, with fixed intervals regardless of actual equipment condition.
**Pain Points:**
- 15% unplanned downtime on critical crushing equipment
- $6M annual maintenance spend with limited visibility into effectiveness
- Reactive culture — fixing failures rather than preventing them
- Sensor data existed but wasn't connected to maintenance decisions
## The Approach
SAS-AM partnered with the maintenance and reliability teams over 8 months to implement a practical condition-based maintenance programme.
**Phase 1: Critical Asset Identification (2 months)**
- Mapped all assets by criticality using risk-based methodology
- Identified 35 critical rotating assets for initial focus
- Documented dominant failure modes for each asset type
**Phase 2: Data Integration (3 months)**
- Connected existing vibration monitoring to asset hierarchy
- Cleaned CMMS data for target assets
- Built initial condition indicators
**Phase 3: Predictive Model Development (3 months)**
- Developed failure prediction models for top 10 failure modes
- Integrated predictions into daily planning workflow
- Trained maintenance planners on interpreting recommendations
**Key Differentiator:** The maintenance team was involved from day one. They helped define what "useful" meant and shaped the system around their actual decision-making process.
## The Outcomes
| Metric | Before | After | Improvement |
|--------|--------|-------|-------------|
| Unplanned downtime | 15% | 11.5% | 23% reduction |
| Maintenance spend | $6M | $4.6M | $1.4M annual savings |
| Bearing failures | 42/year | 28/year | 33% reduction |
| Planner time on reactive work | 60% | 35% | 42% reduction |
> "The difference is we're now making decisions based on what the equipment is telling us, not what the calendar says. The maintenance team trusts the system because they helped build it." — Maintenance Manager
## Key Learnings
1. **Start narrow** — 35 critical assets, not 200+ total assets
2. **Use existing sensors** — no major capital investment required
3. **Involve the team** — buy-in comes from participation, not presentations
4. **Connect to decisions** — predictions are useless without action pathways
## Ready to Achieve Similar Results?
Contact SAS-AM for a practical assessment of your predictive maintenance opportunity. We start with what you have and build from there.
---
**Project Details**: Resources & Minerals, 8 months, Reliability Engineering + Advanced Analytics
references/article-template.md — Article structure templatereferences/case-study-template.md — Case study STAR framework templatereferences/cms-schema.json — Webflow CMS field mappingreferences/seo-checklist.md — SEO optimisation checklistreferences/sector-guidelines.md — Sector-specific messaging guidelinesWhen in doubt, ask: "Would a senior asset management professional find this valuable and credible?" If yes, publish. If not, revise.
npx claudepluginhub sas-asset-management/sasamclaudecodeskills --plugin webflow-content-creatorGuides creation of public SEO content through phases: brief, write, check, and promote. Use for articles, blog posts, landing page copy, and ranking-oriented drafts.
Generates SEO/GEO-optimized content like blogs/pages for web projects by auto-detecting framework, content schema, categories, authors, and brand voice, then validates with geo-lint's 92 rules.
Config-driven SEO content pipeline with dashboard, multi-agent queue, token budgets, and research→write→audit→publish workflow. Supports StoryBrand/Cialdini/JTBD frameworks. Use for batch article generation or scaled publishing.